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During the Fall and Spring semesters, the Computational Social Science (CSS) and the Computational Sciences and Informatics (CSI) Programs have merged their seminar/colloquium series where students, faculty and guest speakers present their latest research. These seminars are free and are open to the public. This series takes place on Fridays from 3-4:30 in Center for Social Complexity Suite which is located on the third floor of Research Hall.

If you would like to join the seminar mailing list please email Karen Underwood.

Notice and InvitationOral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University

Analysis, modeling, and simulation of organ transplantation and donation can enhance the understanding of this complex system and guide strategic policy improvements. Four major research questions are addressed in this work: (1) how can we further enable data-driven research of the transplant system for future scientists?; (2) what demographic factors influence donations and access to transplantation?; (3) how do laws and policies affect organ donations?; and (4) how do certain patient advantages impact the overall system as well as those lacking advantages?

A data pipeline and associated software were developed and published that address how to further data-driven research of the transplant system for future scientists. This software simplifies access to and analysis of data from proprietary Organ Procurement and Transplantation Network (OPTN) Standard Transplant Analysis and Research (STAR) files to an open-source database format. These files contain data on every organ donor, waitlist registrant, and transplant recipient since 1987 in the US. This data pipeline directly facilitated the next phase of research which involved performing an analysis of the transplant system using this dataset. The exploratory data analysis scales transplant data to the relative populations to gain a better understanding of the differences between demographic groups and reveals important differences across education levels, gender, race, and ethnicity.

Demographic factors influencing organ donation and access to transplants are analyzed in this work through exploratory visualizations and predictive modeling. A visual exploratory analysis is presented which examines demographic features of organ donors and highlights differences in intersectional data across the population of donors compared to the relative population described by the US Census. Additionally, a random forest model is used to determine the features of patients on the waitlist for a kidney transplant to determine if certain attributes may inadvertently drive the allocation system. This model predicts patient outcomes based on features represented in the model with an accuracy above the zero-rule baseline. The analysis found that patient age, year of listing, body weight, and zip code are important factors in determining a patient’s outcome – other demographic factors such as race and gender were not important prediction features.

State and local laws, policies, and their impact on organ donation are evaluated through a statistical analysis that compares donations after the implementation of a policy to areas without the policy implementation. A database of state and local laws and policies and the years of implementation was developed to compare donations across the country. The results demonstrated that some policies can be correlated with a change in donation, but only for certain demographic subgroups in a population.

Finally, I built discrete event simulation models of a representative patient population to determine the impact of changes to the transplant system that can not be easily demonstrated in the real world. A transplant process model was developed to determine how increasing living and deceased donation overall and within racial sub-groups would impact the number of donors each year. Additionally, an agent-based queuing model was used to understand the impact of allowing patients to register within more than one area. This model provides a valuable tool for examining policy changes that shows the global and local impacts of multiple listing. The analysis found that multiply listed patients have improved access to transplants and are less likely to die while waiting for a transplant.

Notice and InvitationOral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Science and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University

Theoretical studies of the properties of materials are important as they serve to narrow the focus of what are normally time consuming and costly experimental searches. In modeling these materials, first-principles density functional methods have been proven to quite effective. They have the drawback of being computationally expensive and, to mitigate this, faster approaches have been developed such as the tight-binding model.

We have used the Naval Research Lab (NRL) tight-binding (TB) method to study the electronic and mechanical properties of the noble metals. The tight-binding Hamiltonians are determined from a fit that has a non-orthogonal basis and reproduces the electronic structure and total energy values of first-principles linearized augmented plane wave calculations. In order to perform molecular dynamics simulations, we developed new TB parameters that work well at smaller interatomic distances. We analyze fcc, bcc and sc periodic structures and we demonstrate that the TB parameters are transferable and robust for calculating additional dynamical properties which they had not been fitted to.

To do this, we calculated phonon frequencies and density of states at finite temperature and performed simulations to determine the coefficients of thermal expansion and the atomic mean squared displacement. The energies for vacancy formation were also calculated as were the binding energies for fcc-based, bcc-based and icosahedral clusters of different sizes. The results compared very well with experimental observations and independent first-principles density functional calculations.

Extending from the single element systems, we develop parameter sets for the Cu-Ag and Ag-Au noble metal binary alloys as well. These parameters were fit to the structures 2, 10, 12 − 3,3, with the and representing the different combinations of , and in addition to the fcc , and .

As an output of this extension to the binary systems, the following quantities were reproduced in good agreement with available experimental and theoretical values: elastic constants, densities of electronic states as well as the total energies of additional crystal structures that were not included in the original first-principles database. We also used this TB parametrization for the alloy systems to successfully perform molecular dynamics simulations and determined the energies for vacancy formation, temperature dependence of the coefficient of thermal expansion, the mean squared displacement and phonon spectra. In addition we show that these TB parameters work for determining binding energies and bond lengths of Cu-Ag fcc-like clusters.